---
title: Image Retrieval
---

LlamaParse `json` mode supports extracting any images found in a page object by using the `getImages` function. They are downloaded to a local folder and can then be sent to a multimodal LLM for further processing.

## Installation

```package-install
npm i llamaindex @llamaindex/cloud @llamaindex/openai
```

## Usage

We use the `getImages` method to input our array of JSON objects, download the images to a specified folder and get a list of ImageNodes.

```ts
const reader = new LlamaParseReader();
const jsonObjs = await reader.loadJson("../data/uber_10q_march_2022.pdf");
const imageDicts = await reader.getImages(jsonObjs, "images");
```

### Multimodal Indexing

You can create an index across both text and image nodes by requesting alternative text for the image from a multimodal LLM.

```ts
import { Document, ImageNode, VectorStoreIndex } from "llamaindex";
import { LlamaParseReader } from "@llamaindex/cloud";
import { OpenAI } from "@llamaindex/openai";
import { createMessageContent } from "llamaindex";

const reader = new LlamaParseReader();
async function main() {
  // Load PDF using LlamaParse JSON mode and return an array of json objects
  const jsonObjs = await reader.loadJson("../data/uber_10q_march_2022.pdf");
  // Access the first "pages" (=a single parsed file) object in the array
  const jsonList = jsonObjs[0]["pages"];

  const textDocs = getTextDocs(jsonList);
  const imageTextDocs = await getImageTextDocs(jsonObjs);
  const documents = [...textDocs, ...imageTextDocs];
  // Split text, create embeddings and query the index
  const index = await VectorStoreIndex.fromDocuments(documents);
  const queryEngine = index.asQueryEngine();
  const response = await queryEngine.query({
    query:
      "What does the bar graph titled 'Monthly Active Platform Consumers' show?",
  });

  console.log(response.toString());
}

main().catch(console.error);
```

We use two helper functions to create documents from the text and image nodes provided.

#### Text Documents

To create documents from the text nodes of the json object, we just map the needed values to a new `Document` object. In this case we assign the text as text and the page number as metadata.

```ts
function getTextDocs(jsonList: { text: string; page: number }[]): Document[] {
  return jsonList.map(
    (page) => new Document({ text: page.text, metadata: { page: page.page } }),
  );
}
```

#### Image Documents

To create documents from the images, we need to use a multimodal LLM to generate alt text.

For this we create `ImageNodes` and add them as part of our message.

We can use the `createMessageContent` function to simplify this.

```ts
async function getImageTextDocs(
  jsonObjs: Record<string, any>[],
): Promise<Document[]> {
  const llm = new OpenAI({
    model: "gpt-4o",
    temperature: 0.2,
    maxTokens: 1000,
  });
  const imageDicts = await reader.getImages(jsonObjs, "images");
  const imageDocs = [];

  for (const imageDict of imageDicts) {
    const imageDoc = new ImageNode({ image: imageDict.path });
    const prompt = () => `Describe the image as alt text`;
    const message = await createMessageContent(prompt, [imageDoc]);

    const response = await llm.complete({
      prompt: message,
    });

    const doc = new Document({
      text: response.text,
      metadata: { path: imageDict.path },
    });
    imageDocs.push(doc);
  }

  return imageDocs;
}
```

The returned `imageDocs` have the alt text assigned as text and the image path as metadata.

You can see the full example file [here](https://github.com/run-llama/LlamaIndexTS/blob/main/examples/readers/src/llamaparse-json.ts).

## API Reference

- [LlamaParseReader](/docs/api/classes/LlamaParseReader)
